Admittedly, in this specific simple example, \(\mathfrak {M}_g\)  looks so obvious just by our intuition, and some of the readers might still be wondering why we essentially need such an indirect process to generate \(\mathfrak {M}_g\)  rather than try to directly construct the current epistemic Kripke model. But here the main point is, no matter how simple or how complex \(\mathfrak {M}_g\)  may actually be, to say the very least, any straightforward clues about what the epistemic Kripke model is like can be scarcely found either in the real physical world, or in the narration of some story happened in real world. For instance considering the muddy children puzzle, an ordinary solution would commonly start with a cubic epistemic Kripke model which is almost taken for granted, although this puzzle's literal text — namely some English sentences describing the background settings of the puzzle followed by the conversation between the parent and the children — actually shows no direct hint for any cubic structure at all. As a matter of fact, according to David Lewis' modal realism [1]}, all possible worlds in the epistemic Kripke model are equally real, which could suggest that since we are at our current world, we should not be fully capable of observing other possible worlds as directly as the current world, thus not for the whole epistemic Kripke model, either. Therefore in argumentation theory as well as other fields of application, quite substantially, the intangible epistemic Kripke model is always generated from other concrete observables, such as the argument Kripke model. Even though in relatively easy cases like Example  and the muddy children puzzle, the epistemic Kripke model seems fairly apparent, nonetheless, human's intuition may fail to function well when confronted with much complicated circumstances, and moreover importantly, such vague concept of human intuition possesses little reference value for AI systems to be counted as an effective algorithm. Hence, our approach by the dual theory helps to fill up the above gap through automatically generating the appropriate epistemic Kripke model in a systematical way.
